Speech recognition is the process by which a computer (or
other type of machine) identifies spoken words. Basically, it means
talking to your computer, AND having it correctly recognize what you
are saying. The following definitions are the basics needed for understanding
speech recognition technology. - Utterance
An utterance is the vocalization (speaking) of a word or words that
represent a single meaning to the computer. Utterances can be a
single word, a few words, a sentence, or even multiple sentences.
- Speaker Dependance
Speaker dependent systems are designed around a specific speaker.
They generally are more accurate for the correct speaker, but much
less accurate for other speakers. They assume the speaker will
speak in a consistent voice and tempo. Speaker independent systems
are designed for a variety of speakers. Adaptive systems usually start
as speaker independent systems and utilize training techniques to
adapt to the speaker to increase their recognition accuracy.
- Vocabularies
Vocabularies (or dictionaries) are lists of words or utterances that
can be recognized by the SR system. Generally, smaller vocabularies
are easier for a computer to recognize, while larger vocabularies
are more difficult. Unlike normal dictionaries, each entry doesn't
have to be a single word. They can be as long as a sentence or two.
Smaller vocabularies can have as few as 1 or 2 recognized utterances
(e.g."Wake Up"), while very large vocabularies can have a hundred
thousand or more!
- Accuract
The ability of a recognizer can be examined by measuring its
accuracy - or how well it recognizes utterances. This includes not
only correctly identifying an utterance but also identifying if the
spoken utterance is not in its vocabulary. Good ASR systems have an
accuracy of 98% or more! The acceptable accuracy of a system
really depends on the application.
- Training
Some speech recognizers have the ability to adapt to a speaker.
When the system has this ability, it may allow training to take
place. An ASR system is trained by having the speaker repeat
standard or common phrases and adjusting its comparison algorithms
to match that particular speaker. Training a recognizer usually
improves its accuracy.
Training can also be used by speakers that have difficulty
speaking, or pronouncing certain words. As long as the speaker
can consistently repeat an utterance, ASR systems with training
should be able to adapt.
Speech recognition systems can be separated in several different
classes by describing what types of utterances they have the ability
to recognize. These classes are based on the fact that one of the
difficulties of ASR is the ability to determine when a speaker starts
and finishes an utterance. Most packages can fit into more than one
class, depending on which mode they're using. - Isolated Words
Isolated word recognizers usually require each utterance to have
quiet (lack of an audio signal) on BOTH sides of the sample window.
It doesn't mean that it accepts single words, but does require
a single utterance at a time. Often, these systems have
"Listen/Not-Listen" states, where they require the speaker to wait
between utterances (usually doing processing during the pauses).
Isolated Utterance might be a better name for this class.
- Connected Words
Connect word systems (or more correctly 'connected utterances')
are similar to Isolated words, but allow separate utterances to be
'run-together' with a minimal pause between them.
- Continuous Speech
Continuous recognition is the next step. Recognizers with continuous
speech capabilities are some of the most difficult to create because
they must utilize special methods to determine utterance boundaries.
Continuous speech recognizers allow users to speak almost naturally,
while the computer determines the content. Basically, it's computer
dictation.
- Spontaneous Speech
There appears to be a variety of definitions for what spontaneous
speech actually is. At a basic level, it can be thought of as
speech that is natural sounding and not rehearsed. An ASR system
with spontaneous speech ability should be able to handle a variety
of natural speech features such as words being run together, "ums"
and "ahs", and even slight stutters.
- Voice Verification/Identification
Some ASR systems have the ability to identify specific users. This
document doesn't cover verification or security systems.
Although any task that involves interfacing with a computer can
potentially use ASR, the following applications are the most
common right now. - Dictation
Dictation is the most common use for ASR systems today. This
includes medical transcriptions, legal and business dictation, as
well as general word processing. In some cases special vocabularies
are used to increase the accuracy of the system.
- Command and Control
ASR systems that are designed to perform functions and actions on the
system are defined as Command and Control systems. Utterances like
"Open Netscape" and "Start a new xterm" will do just that.
- Telephony
Some PBX/Voice Mail systems allow callers to speak commands instead of
pressing buttons to send specific tones.
- Wearables
Because inputs are limited for wearable devices, speaking is a
natural possibility.
- Medical/Disabilities
Many people have difficulty typing due to physical limitations such
as repetitive strain injuries (RSI), muscular dystrophy, and
many others. For example, people with difficulty hearing could use
a system connected to their telephone to convert the caller's speech
to text.
- Embedded Applications
Some newer cellular phones include C&C speech recognition that allow
utterances such as "Call Home". This could be a major factor in the
future of ASR and Linux. Why can't I talk to my television yet?
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